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Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

Lim, Hyun Gyu, Rychel, Kevin, Sastry, Anand V., Bentley, Gayle J., Mueller, Joshua, Schindel, Heidi S., Larsen, Peter E., Laible, Philip D., Guss, Adam M., Niu, Wei, Johnson, Christopher W., Beckham, Gregg T., Feist, Adam M., Palsson, Bernhard O.·2022·Metabolic Engineering
DOI 10.1016/j.ymben.2022.04.004

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  1. Bailey, 2009, MEME SUITE: tools for motif discovery and searching, Nucleic Acids Res., 37, W202, 10.1093/nar/gkp335 · DOI · Paper page
  2. Barbosa, 2019, Insights into Klebsiella pneumoniae type VI secretion system transcriptional regulation, BMC Genom., 20, 506, 10.1186/s12864-019-5885-9 · DOI
  3. Bartlett, 2017, Mapping genome-wide transcription-factor binding sites using DAP-seq, Nat. Protoc., 12, 1659, 10.1038/nprot.2017.055 · DOI
  4. Bentley, 2020, Engineering glucose metabolism for enhanced muconic acid production in Pseudomonas putida KT2440, Metab. Eng., 59, 64, 10.1016/j.ymben.2020.01.001 · DOI
  5. Bernal, 2017, The Pseudomonas putida T6SS is a plant warden against phytopathogens, ISME J., 11, 972, 10.1038/ismej.2016.169 · DOI
  6. Blanco-Romero, 2018, Genome-wide analysis of the FleQ direct regulon in Pseudomonas fluorescens F113 and Pseudomonas putida KT2440, Sci. Rep., 8, 13145, 10.1038/s41598-018-31371-z · DOI
  7. Bojanovič, 2017, Global transcriptional responses to osmotic, oxidative, and imipenem stress conditions in Pseudomonas putida, Appl. Environ. Microbiol., 83, 10.1128/AEM.03236-16 · DOI
  8. Chang, 2007, Alginate production by Pseudomonas putida creates a hydrated microenvironment and contributes to biofilm architecture and stress tolerance under water-limiting conditions, J. Bacteriol., 189, 8290, 10.1128/JB.00727-07 · DOI
  9. Chauhan, 2021, Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal Thermoacidophile Sulfolobus acidocaldarius, Front. Microbiol., 12, 753521, 10.3389/fmicb.2021.753521 · DOI
  10. Checovich, 1995, Fluorescence polarization--a new tool for cell and molecular biology, Nature, 375, 254, 10.1038/375254a0 · DOI
  11. Chen, 2020, Type VI secretion system of Pseudomonas aeruginosa is associated with biofilm formation but not environmental adaptation, Microbiol. Open, 9, e991, 10.1002/mbo3.991 · DOI
  12. Comon, 1994, Independent component analysis, A new concept?, Signal Process., 36, 287, 10.1016/0165-1684(94)90029-9 · DOI
  13. Daddaoua, 2009, Regulation of glucose metabolism in Pseudomonas: the phosphorylative branch and entner-doudoroff enzymes are regulated by a repressor containing a sugar isomerase domain, J. Biol. Chem., 284, 21360, 10.1074/jbc.M109.014555 · DOI
  14. Del Castillo, 2008, A set of activators and repressors control peripheral glucose pathways in Pseudomonas putida to yield a common central intermediate, J. Bacteriol., 190, 2331, 10.1128/JB.01726-07 · DOI
  15. Di Tommaso, 2017, Nextflow enables reproducible computational workflows, Nat. Biotechnol., 35, 316, 10.1038/nbt.3820 · DOI · Paper page
  16. Diez, 2020, The alarmones (p)ppGpp directly regulate translation initiation during entry into quiescence, Proc. Natl. Acad. Sci. U.S.A., 117, 15565, 10.1073/pnas.1920013117 · DOI
  17. Ester, 1996, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, In Proceedings of 2nd International Conference on KDD, 226
  18. Ewels, 2016, MultiQC: summarize analysis results for multiple tools and samples in a single report, Bioinformatics, 32, 3047, 10.1093/bioinformatics/btw354 · DOI · Paper page
  19. Galperin, 2015, Expanded microbial genome coverage and improved protein family annotation in the COG database, Nucleic Acids Res., 43, D261, 10.1093/nar/gku1223 · DOI
  20. Gao, 2021, Unraveling the functions of uncharacterized transcription factors in Escherichia coli using ChIP-exo, Nucleic Acids Res., 49, 9696, 10.1093/nar/gkab735 · DOI
  21. Guo, 1999, PcaR-mediated activation and repression of pca genes from Pseudomonas putida are propagated by its binding to both the -35 and the -10 promoter elements, Mol. Microbiol., 32, 253, 10.1046/j.1365-2958.1999.01342.x · DOI
  22. Gupta, 2007, Quantifying similarity between motifs, Genome Biol., 8, R24, 10.1186/gb-2007-8-2-r24 · DOI · Paper page
  23. Hellman, 2007, Electrophoretic mobility shift assay (EMSA) for detecting protein-nucleic acid interactions, Nat. Protoc., 2, 1849, 10.1038/nprot.2007.249 · DOI
  24. Herrgård, 2004, Reconstruction of microbial transcriptional regulatory networks, Curr. Opin. Biotechnol., 15, 70, 10.1016/j.copbio.2003.11.002 · DOI
  25. Hervás, 2009, NtrC-dependent regulatory network for nitrogen assimilation in Pseudomonas putida, J. Bacteriol., 191, 6123, 10.1128/JB.00744-09 · DOI
  26. Huang, 2019, An integrated genomic regulatory network of virulence-related transcriptional factors in Pseudomonas aeruginosa, Nat. Commun., 10, 2931, 10.1038/s41467-019-10778-w · DOI
  27. Huerta-Cepas, 2019, eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses, Nucleic Acids Res., 47, D309, 10.1093/nar/gky1085 · DOI
  28. Hyvärinen, 1999, Fast and robust fixed-point algorithms for independent component analysis, IEEE Trans. Neural Network., 10, 626, 10.1109/72.761722 · DOI
  29. Jayakody, 2018, Thermochemical wastewater valorization via enhanced microbial toxicity tolerance, Energy Environ. Sci., 11, 1625, 10.1039/C8EE00460A · DOI
  30. Johnson, 2019, Innovative chemicals and materials from bacterial aromatic catabolic pathways, Joule, 10.1016/j.joule.2019.05.011 · DOI
  31. Karp, 2019, The BioCyc collection of microbial genomes and metabolic pathways, Briefings Bioinf., 20, 1085, 10.1093/bib/bbx085 · DOI
  32. Kiliç, 2014, CollecTF: a database of experimentally validated transcription factor-binding sites in Bacteria, Nucleic Acids Res., 42, D156, 10.1093/nar/gkt1123 · DOI
  33. Lamoureux, 2021, PRECISE 2.0: an expanded high-quality RNA-seq compendium for Escherichia coli K-12 reveals high-resolution transcriptional regulatory structure, bioRxiv
  34. Langmead, 2009, Ultrafast and memory-efficient alignment of short DNA sequences to the human genome, Genome Biol., 10, R25, 10.1186/gb-2009-10-3-r25 · DOI · Paper page
  35. Lei, 2020, Determining the different mechanisms used by pseudomonas species to cope with minimal inhibitory concentrations of zinc via comparative transcriptomic analyses, Front. Microbiol., 11, 573857, 10.3389/fmicb.2020.573857 · DOI
  36. Liao, 2014, featureCounts: an efficient general purpose program for assigning sequence reads to genomic features, Bioinformatics, 30, 923, 10.1093/bioinformatics/btt656 · DOI · Paper page
  37. Liebermeister, 2002, Linear modes of gene expression determined by independent component analysis, Bioinformatics, 18, 51, 10.1093/bioinformatics/18.1.51 · DOI
  38. Lim, 2013, The effect of zinc limitation on the transcriptome of Pseudomonas protegens Pf-5, Environ. Microbiol., 15, 702, 10.1111/j.1462-2920.2012.02849.x · DOI
  39. Lim, 2021, Generation of Pseudomonas putida KT2440 strains with efficient utilization of xylose and galactose via adaptive laboratory evolution, ACS Sustain. Chem. Eng., 9, 11512, 10.1021/acssuschemeng.1c03765 · DOI
  40. Lim, 2020, Generation of ionic liquid tolerant Pseudomonas putida KT2440 strains via adaptive laboratory evolution, Green Chem., 22, 5677, 10.1039/D0GC01663B · DOI
  41. Lundblad, 1996, Fluorescence polarization analysis of protein-DNA and protein-protein interactions, Mol. Endocrinol., 10, 607
  42. McConn, 2021, Optimal dimensionality selection for independent component analysis of transcriptomic data, BMC Bioinf., 22, 584, 10.1186/s12859-021-04497-7 · DOI
  43. Miyazaki, 2018, Physiological and transcriptome changes induced by Pseudomonas putida acquisition of an integrative and conjugative element, Sci. Rep., 8, 5550, 10.1038/s41598-018-23858-6 · DOI
  44. Molina-Henares, 2017, FleQ of Pseudomonas putida KT2440 is a multimeric cyclic diguanylate binding protein that differentially regulates expression of biofilm matrix components, Res. Microbiol., 168, 36, 10.1016/j.resmic.2016.07.005 · DOI
  45. Nakamura, 2020, H-NS family proteins drastically change their targets in response to the horizontal transfer of the catabolic plasmid pCAR1, Front. Microbiol., 11, 1099, 10.3389/fmicb.2020.01099 · DOI
  46. Nielsen, 2011, Cell-cell and cell-surface interactions mediated by cellulose and a novel exopolysaccharide contribute to Pseudomonas putida biofilm formation and fitness under water-limiting conditions, Environ. Microbiol., 13, 1342, 10.1111/j.1462-2920.2011.02432.x · DOI
  47. Nikel, 2018, Pseudomonas putida as a functional chassis for industrial biocatalysis: from native biochemistry to trans-metabolism, Metab. Eng., 50, 142, 10.1016/j.ymben.2018.05.005 · DOI · Paper page
  48. Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825
  49. Pobre, 2020, Prediction of novel non-coding RNAs relevant for the growth of Pseudomonas putida in a bioreactor, Microbiology, 166, 149, 10.1099/mic.0.000875 · DOI
  50. Poudel, 2020, Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators, and role in key physiological response, Proc. Natl. Acad. Sci. U.S.A., 117, 17228, 10.1073/pnas.2008413117 · DOI
  51. Rajput, 2021, Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators, bioRxiv
  52. Rinas, 1995, Entry of Escherichia coli into stationary phase is indicated by endogenous and exogenous accumulation of nucleobases, Appl. Environ. Microbiol., 61, 4147, 10.1128/aem.61.12.4147-4151.1995 · DOI
  53. Rychel, 2021, iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning, Nucleic Acids Res., 49, D112, 10.1093/nar/gkaa810 · DOI
  54. Rychel, 2020, Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome, Nat. Commun., 11, 6338, 10.1038/s41467-020-20153-9 · DOI
  55. Saelens, 2018, A comprehensive evaluation of module detection methods for gene expression data, Nat. Commun., 9, 1090, 10.1038/s41467-018-03424-4 · DOI
  56. Santos-Zavaleta, 2019, RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12, Nucleic Acids Res., 47, D212, 10.1093/nar/gky1077 · DOI
  57. Sastry, 2019, The Escherichia coli transcriptome mostly consists of independently regulated modules, Nat. Commun., 10, 5536, 10.1038/s41467-019-13483-w · DOI
  58. Sastry, 2021, Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks, bioRxiv
  59. Smith, 2010, Economical evolution: microbes reduce the synthetic cost of extracellular proteins, mBio, 1, 10.1128/mBio.00131-10 · DOI
  60. Szklarczyk, 2019, STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47, D607, 10.1093/nar/gky1131 · DOI · Paper page
  61. Wackett, 2003, Pseudomonas putida--a versatile biocatalyst, Nat. Biotechnol., 21, 136, 10.1038/nbt0203-136 · DOI
  62. Wang, 2012, RSeQC: quality control of RNA-seq experiments, Bioinformatics, 28, 2184, 10.1093/bioinformatics/bts356 · DOI
  63. Winsor, 2016, Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database, Nucleic Acids Res., 44, D646, 10.1093/nar/gkv1227 · DOI
  64. Xiao, 2018, FinR regulates expression of nicC and nicX operons, involved in nicotinic acid degradation in Pseudomonas putida KT2440, Appl. Environ. Microbiol., 84, 10.1128/AEM.01210-18 · DOI
  65. Yoo, 2022, Machine learning of all Mycobacterium tuberculosis H37Rv RNA-seq data reveals a structured interplay between metabolism, stress response, and infection, mSphere, 10.1128/msphere.00033-22 · DOI

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Metadata

Authors
Lim, H. G., Rychel, K., Sastry, A. V., Bentley, G. J., Mueller, J., Schindel, H. S., Larsen, P. E., Laible, P. D., Guss, A. M., Niu, W., Johnson, C. W., Beckham, G. T., Feist, A. M., & Palsson, B. O.
Venue
Metabolic Engineering
Publisher
Elsevier BV
Published
2022-07
Volume
72
Pages
297-310
ISSN
1096-7176
DOI
10.1016/j.ymben.2022.04.004
Cited by
80
References
2
Language
en
Licence
Funders
  • Office of Energy Efficiency and Renewable Energy (DE-AC36-08GO28308)
  • University of Nebraska-Lincoln Nebraska Center for Energy Sciences Research
  • US Department of Energy Office of Science (DE-AC02-05CH11231, DE-AC05-00OR22725)
  • Biological and Environmental Research
  • U.S. Department of Energy
  • Bioenergy Technologies Office
  • National Renewable Energy Laboratory

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